// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen3@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC                cxx11_tensor_broadcast_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

static void test_broadcast_sycl(const Eigen::SyclDevice& sycl_device)
{

    // BROADCAST test:
    array<int, 4> in_range   = {{2, 3, 5, 7}};
    array<int, 4> broadcasts = {{2, 3, 1, 4}};
    array<int, 4> out_range;   // = in_range * broadcasts
    for ( size_t i = 0; i < out_range.size(); ++i )
        out_range[i] = in_range[i] * broadcasts[i];

    Tensor<float, 4> input(in_range);
    Tensor<float, 4> out(out_range);

    for ( size_t i = 0; i < in_range.size(); ++i )
        VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);


    for ( int i = 0; i < input.size(); ++i )
        input(i) = static_cast<float>(i);

    float* gpu_in_data  = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize() * sizeof(float)));
    float* gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(float)));

    TensorMap<Tensor<float, 4>> gpu_in(gpu_in_data, in_range);
    TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range);
    sycl_device.memcpyHostToDevice(gpu_in_data, input.data(), (input.dimensions().TotalSize()) * sizeof(float));
    gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));

    for ( int i = 0; i < 4; ++i ) {
        for ( int j = 0; j < 9; ++j ) {
            for ( int k = 0; k < 5; ++k ) {
                for ( int l = 0; l < 28; ++l ) {
                    VERIFY_IS_APPROX(input(i % 2, j % 3, k % 5, l % 7), out(i, j, k, l));
                }
            }
        }
    }
    printf("Broadcast Test Passed\n");
    sycl_device.deallocate(gpu_in_data);
    sycl_device.deallocate(gpu_out_data);
}

void test_cxx11_tensor_broadcast_sycl()
{
    cl::sycl::gpu_selector s;
    Eigen::SyclDevice      sycl_device(s);
    CALL_SUBTEST(test_broadcast_sycl(sycl_device));
}
